LLM Application Frameworks

Large language models by themselves are less than meets the eye, with the moniker “stochastic parrots” being an accurate description [1]. However, when connected to specific data for retrieval-augmented generation, they become more reliable and less likely to produce inaccurate results. This is where LLM application frameworks come in, providing the necessary plumbing to tie components together and orchestrate them [1]. According to [1], LLM application frameworks like LangChain, LlamaIndex, Semantic Kernel, and Haystack help construct retrieval-augmented generation and other AI-enabled apps using favorite large language models.

Key takeaways

• LLM application frameworks reduce the amount of code needed to create an application [1].
• Use cases for LLM application frameworks include retrieval-augmented generation, chatbots, agents, and generative multi-modal question answering [1].
• Programming languages supported by LLM application frameworks include Python, C#, Java, TypeScript, and JavaScript [1].
• Haystack is an open-source framework for building LLM applications, with integrations including models hosted on platforms like Hugging Face and OpenAI [1].
• The Open Worldwide Application Security Project (OWASP) has updated its list of the top 10 most critical vulnerabilities in LLM applications, including prompt injection and supply chain vulnerabilities [2].

LLM Application Frameworks and Their Use Cases

LLM application frameworks are designed to help reduce the amount of code needed to create an application, with the fact that these frameworks have been designed and coded by experts, tested by thousands of programmers and users, and used in production, giving confidence that the “plumbing” will perform correctly [1]. The use cases for LLM application frameworks are varied, including retrieval-augmented generation, chatbots, agents, and generative multi-modal question answering [1]. For example, retrieval-augmented generation is a way to expand the knowledge of an LLM without retraining or fine-tuning the LLM, by retrieving information from a specified source, augmenting the prompt with the context retrieved from the source, and then generating using the model and the augmented prompt [1].

Security Risks and Frameworks

However, with the increasing adoption of LLMs, security risks are also becoming more prominent, with the OWASP updating its list of the top 10 most critical vulnerabilities in LLM applications [2]. According to [2], prompt injection and supply chain vulnerabilities remain the main LLM vulnerabilities, but new risks are emerging, including system prompt leakage and misinformation. The list aims to educate developers, designers, architects, managers, and organizations about the potential security risks when deploying and managing LLMs, raising awareness of vulnerabilities, suggesting remediation strategies, and providing a framework for secure LLM application development [2].